Affiliation with deviant peers is known to increase the risk of substance use, antisocial behavior, and risky sexual practices during adolescence and into adulthood. We aim to develop models that forecast the risk of such problems from exposure to a given adolescent peer social environment, and suggest practical preventative measures available to parents, schools, and communities. To achieve this ambitious but vital prevention goal, much more needs to be known about how and when peer influence and selection occur. This project will examine mechanisms that theory and prior research suggest may promote or inhibit peer group influence for substance use, antisocial behavior, and risky sex among adolescents age 14-17. Hypotheses draw heavily upon social learning theory, with the fundamental premise that adolescents who use alcohol and drugs and otherwise misbehave do so largely because of the resulting social rewards. This project seeks to track and model the effects of the social reward contingencies associated with these often-related classes of problem behaviors. Using a unique combination of school-based longitudinal social network, Ecological Momentary Assessment (EMA), and survey methodologies, we seek to identify the nature of study participants' actual peer dynamics, as reported in real time, from random EMAs on programmed iPod devices. Measures include mood, perceptions of peer popularity, peer victimization, activities, and parent factors (monitoring, rule setting, and parent-child relationship). These moderators will contextualize social relationships known from network data, and are modeled using the stochastic actor-based modeling framework as implemented in the RSiena analysis software. The project will directly contribute to NIDA's mission to reduce initiation of drug and alcohol use (which normally occurs during adolescence) and abuse/addiction, as well as the risky sexual practices accompanying a substance-abusing lifestyle that can expose youth to HIV and other serious health hazards. It will also contribute to social-network-based models of peer affiliations and problem behavior.